Spatiotemporal data mining for situation awareness in microblogs

Özdikiş, Özer
Detection of real-world events using messages posted in microblogs has been the motivation of numerous recent studies. In this thesis, we study spatiotemporal data mining techniques to improve situation awareness by detecting events and estimating their locations using the content in microblogs, particularly in Twitter. We present an enhancement to the clustering techniques in the literature by measuring associations between terms in tweets in a temporal context and using these associations in a vector expansion process to improve the accuracy of online tweet clustering and event detection. Moreover, we propose a method using the Dempster-Shafer theory to estimate the locations of the detected events. We utilize three basic location-related features in tweets, namely the latitude-longitude metadata in geotagged tweets, the location names mentioned in the tweet content and the location attribute in the user profile, as independent sources of evidence. We apply combination rules in the Dempster-Shafer theory to fuse them into a single model, and estimate the whereabouts of a detected event. We demonstrate the results of our experiments for event detection and location estimation using public tweets posted in Turkey. Our experiments indicate higher success rates than those obtained by the state of the art methods.


Evidential estimation of event locations in microblogs using the Dempster-Shafer theory
Ozdikis, Ozer; Ogurtuzun, Halit; Karagöz, Pınar (2016-11-01)
Detecting real-world events by following posts in microblogs has been the motivation of numerous recent studies. In this work, we focus on the spatio-temporal characteristics of events detected in microblogs, and propose a method to estimate their locations using the Dempster-Shafer theory. We utilize three basic location-related features of the posts, namely the latitude-longitude metadata provided by the GPS sensor of the user's device, the textual content of the post, and the location attribute in the us...
Event detection on social media using transaction based stream processing engine
Çınar, Hüseyin Alper; Karagöz, Pınar; Department of Computer Engineering (2019)
The aim of this study is detecting events on social media by improving current solutions in terms of accuracy and time performance. An event is something that occurs in a short duration of time in a certain place. In this thesis, the problem is modelled as a streaming transaction process. Three different event detection method is adapted to our solution. First one is the keyword-based event detection method that looks for bursty keywords in a period. The second one is the clustering-based event detection me...
Online event detection from streaming data
Şahin, Özlem Ceren; Karagöz, Pınar; Department of Computer Engineering (2018)
The purpose of this study is detecting events from social media in an online fashion where event is a happening that takes place at a certain time and place that attracts attention within a short period of time. By doing so, it is aimed to provide a system both accurate and efficient at the same time. The problem studied in this thesis is modeled as a stream processing problem and three alternative methods are proposed. The first event detection method is keyword-based and works with bursty keywords inside ...
Event Detection via Tracking the Change in Community Structure and Communication Trends
Aktunc, Riza; Karagöz, Pınar; Toroslu, Ismail Hakki (2022-01-01)
Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change trac...
Event Detection by Change Tracking on Community Structure of Temporal Networks
Aktunc, Riza; Toroslu, İsmail Hakkı; Karagöz, Pınar (2018-08-31)
Event detection is a popular research problem, aiming to detect events from online data sources with least possible delay. Most of the previous work focus on analyzing textual content such as social media postings to detect happenings. In this work, we consider event detection as a change detection problem in network structure, and propose a method that detects change in community structure extracted from communication network. We study three versions of the method based on different change models. Experime...
Citation Formats
Ö. Özdikiş, “Spatiotemporal data mining for situation awareness in microblogs,” Ph.D. - Doctoral Program, Middle East Technical University, 2016.